Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations260975
Missing cells628503
Missing cells (%)10.0%
Duplicate rows61995
Duplicate rows (%)23.8%
Total size in memory49.8 MiB
Average record size in memory200.0 B

Variable types

Text10
Numeric5
Unsupported2
Categorical7

Alerts

aff_code has constant value "national"Constant
page_channel has constant value "shopping"Constant
Dataset has 61995 (23.8%) duplicate rowsDuplicates
drivetrain is highly imbalanced (52.6%)Imbalance
fuel_type is highly imbalanced (81.1%)Imbalance
msrp has 63798 (24.4%) missing valuesMissing
local_zone has 260975 (100.0%) missing valuesMissing
interior_color has 13419 (5.1%) missing valuesMissing
price_badge has 260975 (100.0%) missing valuesMissing
trim has 4179 (1.6%) missing valuesMissing
dealer_name has 2693 (1.0%) missing valuesMissing
dealer_zip has 2693 (1.0%) missing valuesMissing
mileage has 5943 (2.3%) missing valuesMissing
cat has 4057 (1.6%) missing valuesMissing
exterior_color has 3285 (1.3%) missing valuesMissing
price is highly skewed (γ1 = 359.5290972)Skewed
local_zone is an unsupported type, check if it needs cleaning or further analysisUnsupported
price_badge is an unsupported type, check if it needs cleaning or further analysisUnsupported
msrp has 65198 (25.0%) zerosZeros
mileage has 10145 (3.9%) zerosZeros

Reproduction

Analysis started2024-07-21 13:40:25.622318
Analysis finished2024-07-21 13:40:39.809800
Duration14.19 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct93439
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:40.038452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters9395100
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33719 ?
Unique (%)12.9%

Sample

1st row2f77722f-3a80-4960-bd04-4859b4df975e
2nd row067f9671-3672-4d0a-afe8-110b905bed3a
3rd row0a24aeeb-112c-4a6f-b932-07504e82dacb
4th row2636cae0-4081-4ce3-8940-5687e8ada129
5th rowb443a216-8ea4-4e1c-b5f3-a6210d6f95e9
ValueCountFrequency (%)
743d3e93-bfdd-4df8-ac72-be6bba76ca70 40
 
< 0.1%
8959974d-c353-40a0-ad49-af524e14d9ed 39
 
< 0.1%
ef89f280-8bee-44f7-b6a7-836c7f51dcc3 38
 
< 0.1%
2acb94ed-1ed9-4ff4-97c7-58dd23794659 38
 
< 0.1%
0da5d4d7-714c-43d0-9cf7-c0e57895fbf8 38
 
< 0.1%
7a580c37-c5ea-4e7a-a1ac-7c78d44dde2a 38
 
< 0.1%
8bc4fa9b-f2cc-4b16-b705-85d3a138475f 38
 
< 0.1%
b7e64e4f-b5f2-4c00-a6f2-40bb9150c2d4 38
 
< 0.1%
41ce1607-5d74-4d82-a6e4-75fa06f00e44 37
 
< 0.1%
97e39f52-395b-4561-82f0-9a7f5307d44d 37
 
< 0.1%
Other values (93429) 260594
99.9%
2024-07-21T08:40:40.566814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1043900
 
11.1%
4 749171
 
8.0%
b 556422
 
5.9%
8 555463
 
5.9%
a 554473
 
5.9%
9 552933
 
5.9%
1 491443
 
5.2%
3 491438
 
5.2%
e 490317
 
5.2%
d 489810
 
5.2%
Other values (7) 3419730
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9395100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1043900
 
11.1%
4 749171
 
8.0%
b 556422
 
5.9%
8 555463
 
5.9%
a 554473
 
5.9%
9 552933
 
5.9%
1 491443
 
5.2%
3 491438
 
5.2%
e 490317
 
5.2%
d 489810
 
5.2%
Other values (7) 3419730
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9395100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1043900
 
11.1%
4 749171
 
8.0%
b 556422
 
5.9%
8 555463
 
5.9%
a 554473
 
5.9%
9 552933
 
5.9%
1 491443
 
5.2%
3 491438
 
5.2%
e 490317
 
5.2%
d 489810
 
5.2%
Other values (7) 3419730
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9395100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1043900
 
11.1%
4 749171
 
8.0%
b 556422
 
5.9%
8 555463
 
5.9%
a 554473
 
5.9%
9 552933
 
5.9%
1 491443
 
5.2%
3 491438
 
5.2%
e 490317
 
5.2%
d 489810
 
5.2%
Other values (7) 3419730
36.4%

msrp
Real number (ℝ)

MISSING  ZEROS 

Distinct16080
Distinct (%)8.2%
Missing63798
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean34935.524
Minimum0
Maximum489716
Zeros65198
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:40.721351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34633
Q354395
95-th percentile86890
Maximum489716
Range489716
Interquartile range (IQR)54395

Descriptive statistics

Standard deviation32417.017
Coefficient of variation (CV)0.92790985
Kurtosis5.6903852
Mean34935.524
Median Absolute Deviation (MAD)26364
Skewness1.3293331
Sum6.8884818 × 109
Variance1.050863 × 109
MonotonicityNot monotonic
2024-07-21T08:40:40.858480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65198
25.0%
54595 636
 
0.2%
46465 574
 
0.2%
44115 543
 
0.2%
34085 402
 
0.2%
32189 305
 
0.1%
36926 281
 
0.1%
35975 276
 
0.1%
33160 262
 
0.1%
32360 255
 
0.1%
Other values (16070) 128445
49.2%
(Missing) 63798
24.4%
ValueCountFrequency (%)
0 65198
25.0%
4895 3
 
< 0.1%
5895 1
 
< 0.1%
5985 1
 
< 0.1%
5991 1
 
< 0.1%
5995 2
 
< 0.1%
6000 3
 
< 0.1%
6188 2
 
< 0.1%
6493 4
 
< 0.1%
6495 3
 
< 0.1%
ValueCountFrequency (%)
489716 1
 
< 0.1%
405750 2
 
< 0.1%
367820 3
< 0.1%
359785 3
< 0.1%
353345 1
 
< 0.1%
344375 4
< 0.1%
335000 1
 
< 0.1%
330000 2
 
< 0.1%
329486 7
< 0.1%
326445 5
< 0.1%

year
Real number (ℝ)

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.2839
Minimum1936
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:40.995340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile2008
Q12019
median2023
Q32024
95-th percentile2024
Maximum2025
Range89
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.4423533
Coefficient of variation (CV)0.0031888356
Kurtosis20.134154
Mean2020.2839
Median Absolute Deviation (MAD)1
Skewness-3.5307972
Sum5.2724359 × 108
Variance41.503916
MonotonicityNot monotonic
2024-07-21T08:40:41.128912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 120367
46.1%
2021 19682
 
7.5%
2023 19624
 
7.5%
2020 11983
 
4.6%
2022 11899
 
4.6%
2019 10039
 
3.8%
2018 9115
 
3.5%
2017 7966
 
3.1%
2016 6970
 
2.7%
2015 5716
 
2.2%
Other values (61) 37614
 
14.4%
ValueCountFrequency (%)
1936 7
 
< 0.1%
1941 8
 
< 0.1%
1953 1
 
< 0.1%
1957 65
< 0.1%
1959 44
< 0.1%
1960 44
< 0.1%
1961 10
 
< 0.1%
1962 2
 
< 0.1%
1963 16
 
< 0.1%
1964 28
< 0.1%
ValueCountFrequency (%)
2025 4794
 
1.8%
2024 120367
46.1%
2023 19624
 
7.5%
2022 11899
 
4.6%
2021 19682
 
7.5%
2020 11983
 
4.6%
2019 10039
 
3.8%
2018 9115
 
3.5%
2017 7966
 
3.1%
2016 6970
 
2.7%
Distinct16713
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:41.365634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length102
Median length92
Mean length28.227248
Min length11

Characters and Unicode

Total characters7366606
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2469 ?
Unique (%)0.9%

Sample

1st rowChevrolet:Blazer EV:RS:2024
2nd rowRAM:ProMaster 2500:High Roof:2024
3rd rowMercedes-Benz:Sprinter 2500:High Roof:2024
4th rowHonda:CR-V:EX:2024
5th rowChevrolet:Equinox:LS:2024
ValueCountFrequency (%)
jeep:grand 6340
 
1.3%
s 4930
 
1.0%
premium 4449
 
0.9%
se:2024 3985
 
0.8%
chevrolet:silverado 3925
 
0.8%
ram:promaster 3594
 
0.7%
mercedes-benz:amg 3481
 
0.7%
mercedes-benz:sprinter 3375
 
0.7%
cherokee 3107
 
0.6%
roof:2024 2976
 
0.6%
Other values (14924) 466916
92.1%
2024-07-21T08:40:41.763494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 782928
 
10.6%
2 500085
 
6.8%
e 482194
 
6.5%
0 394043
 
5.3%
a 350510
 
4.8%
r 337781
 
4.6%
o 273351
 
3.7%
i 261702
 
3.6%
245589
 
3.3%
n 213922
 
2.9%
Other values (71) 3524501
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7366606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 782928
 
10.6%
2 500085
 
6.8%
e 482194
 
6.5%
0 394043
 
5.3%
a 350510
 
4.8%
r 337781
 
4.6%
o 273351
 
3.7%
i 261702
 
3.6%
245589
 
3.3%
n 213922
 
2.9%
Other values (71) 3524501
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7366606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 782928
 
10.6%
2 500085
 
6.8%
e 482194
 
6.5%
0 394043
 
5.3%
a 350510
 
4.8%
r 337781
 
4.6%
o 273351
 
3.7%
i 261702
 
3.6%
245589
 
3.3%
n 213922
 
2.9%
Other values (71) 3524501
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7366606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 782928
 
10.6%
2 500085
 
6.8%
e 482194
 
6.5%
0 394043
 
5.3%
a 350510
 
4.8%
r 337781
 
4.6%
o 273351
 
3.7%
i 261702
 
3.6%
245589
 
3.3%
n 213922
 
2.9%
Other values (71) 3524501
47.8%

model
Text

Distinct1159
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:42.145037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length28
Median length25
Mean length7.2717847
Min length1

Characters and Unicode

Total characters1897754
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowBlazer EV
2nd rowProMaster 2500
3rd rowSprinter 2500
4th rowCR-V
5th rowEquinox
ValueCountFrequency (%)
1500 8445
 
2.4%
cherokee 7220
 
2.0%
2500 7173
 
2.0%
grand 6847
 
1.9%
escape 4591
 
1.3%
hybrid 4019
 
1.1%
silverado 3925
 
1.1%
sport 3847
 
1.1%
compass 3600
 
1.0%
promaster 3594
 
1.0%
Other values (935) 299634
84.9%
2024-07-21T08:40:42.625915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 140261
 
7.4%
r 138199
 
7.3%
e 132075
 
7.0%
o 97651
 
5.1%
91920
 
4.8%
n 81160
 
4.3%
0 81133
 
4.3%
i 71312
 
3.8%
t 68421
 
3.6%
s 63380
 
3.3%
Other values (61) 932242
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1897754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 140261
 
7.4%
r 138199
 
7.3%
e 132075
 
7.0%
o 97651
 
5.1%
91920
 
4.8%
n 81160
 
4.3%
0 81133
 
4.3%
i 71312
 
3.8%
t 68421
 
3.6%
s 63380
 
3.3%
Other values (61) 932242
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1897754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 140261
 
7.4%
r 138199
 
7.3%
e 132075
 
7.0%
o 97651
 
5.1%
91920
 
4.8%
n 81160
 
4.3%
0 81133
 
4.3%
i 71312
 
3.8%
t 68421
 
3.6%
s 63380
 
3.3%
Other values (61) 932242
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1897754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 140261
 
7.4%
r 138199
 
7.3%
e 132075
 
7.0%
o 97651
 
5.1%
91920
 
4.8%
n 81160
 
4.3%
0 81133
 
4.3%
i 71312
 
3.8%
t 68421
 
3.6%
s 63380
 
3.3%
Other values (61) 932242
49.1%

local_zone
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing260975
Missing (%)100.0%
Memory size4.0 MiB

interior_color
Text

MISSING 

Distinct2970
Distinct (%)1.2%
Missing13419
Missing (%)5.1%
Memory size4.0 MiB
2024-07-21T08:40:42.959822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length94
Median length5
Mean length7.7821503
Min length1

Characters and Unicode

Total characters1926518
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)0.2%

Sample

1st rowBlack
2nd rowBlack
3rd rowGray
4th rowMedium Ash Gray
5th rowPearl Beige
ValueCountFrequency (%)
black 138507
39.6%
gray 23827
 
6.8%
ebony 17981
 
5.1%
jet 17491
 
5.0%
charcoal 11054
 
3.2%
beige 7150
 
2.0%
red 6082
 
1.7%
dark 5227
 
1.5%
medium 5019
 
1.4%
4382
 
1.3%
Other values (1754) 112864
32.3%
2024-07-21T08:40:43.446762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 252176
13.1%
l 183504
 
9.5%
c 174673
 
9.1%
B 164929
 
8.6%
k 153088
 
7.9%
102064
 
5.3%
e 100057
 
5.2%
r 81079
 
4.2%
o 66661
 
3.5%
t 63100
 
3.3%
Other values (64) 585187
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1926518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 252176
13.1%
l 183504
 
9.5%
c 174673
 
9.1%
B 164929
 
8.6%
k 153088
 
7.9%
102064
 
5.3%
e 100057
 
5.2%
r 81079
 
4.2%
o 66661
 
3.5%
t 63100
 
3.3%
Other values (64) 585187
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1926518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 252176
13.1%
l 183504
 
9.5%
c 174673
 
9.1%
B 164929
 
8.6%
k 153088
 
7.9%
102064
 
5.3%
e 100057
 
5.2%
r 81079
 
4.2%
o 66661
 
3.5%
t 63100
 
3.3%
Other values (64) 585187
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1926518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 252176
13.1%
l 183504
 
9.5%
c 174673
 
9.1%
B 164929
 
8.6%
k 153088
 
7.9%
102064
 
5.3%
e 100057
 
5.2%
r 81079
 
4.2%
o 66661
 
3.5%
t 63100
 
3.3%
Other values (64) 585187
30.4%

aff_code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
national
260975 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2087800
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownational
2nd rownational
3rd rownational
4th rownational
5th rownational

Common Values

ValueCountFrequency (%)
national 260975
100.0%

Length

2024-07-21T08:40:43.584306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-21T08:40:43.676686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
national 260975
100.0%

Most occurring characters

ValueCountFrequency (%)
n 521950
25.0%
a 521950
25.0%
t 260975
12.5%
i 260975
12.5%
o 260975
12.5%
l 260975
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 521950
25.0%
a 521950
25.0%
t 260975
12.5%
i 260975
12.5%
o 260975
12.5%
l 260975
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 521950
25.0%
a 521950
25.0%
t 260975
12.5%
i 260975
12.5%
o 260975
12.5%
l 260975
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 521950
25.0%
a 521950
25.0%
t 260975
12.5%
i 260975
12.5%
o 260975
12.5%
l 260975
12.5%

price
Real number (ℝ)

SKEWED 

Distinct38130
Distinct (%)14.7%
Missing1548
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean46273.637
Minimum0
Maximum4.49954 × 108
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:43.785946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9995
Q123328
median34487
Q351664
95-th percentile92800
Maximum4.49954 × 108
Range4.49954 × 108
Interquartile range (IQR)28336

Descriptive statistics

Standard deviation1249929.7
Coefficient of variation (CV)27.011701
Kurtosis129410.03
Mean46273.637
Median Absolute Deviation (MAD)13601
Skewness359.5291
Sum1.2004631 × 1010
Variance1.5623242 × 1012
MonotonicityNot monotonic
2024-07-21T08:40:43.925059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9995 660
 
0.3%
18995 649
 
0.2%
19995 607
 
0.2%
16995 597
 
0.2%
15995 589
 
0.2%
17995 582
 
0.2%
14995 573
 
0.2%
12995 560
 
0.2%
13995 554
 
0.2%
21995 529
 
0.2%
Other values (38120) 253527
97.1%
(Missing) 1548
 
0.6%
ValueCountFrequency (%)
0 96
< 0.1%
32 1
 
< 0.1%
1234 3
 
< 0.1%
1500 25
 
< 0.1%
1700 2
 
< 0.1%
1750 3
 
< 0.1%
1900 6
 
< 0.1%
1995 6
 
< 0.1%
2000 33
 
< 0.1%
2095 1
 
< 0.1%
ValueCountFrequency (%)
449953995 2
 
< 0.1%
1699800 36
< 0.1%
1099999 5
 
< 0.1%
934900 2
 
< 0.1%
929895 12
 
< 0.1%
869800 7
 
< 0.1%
829800 9
 
< 0.1%
749900 3
 
< 0.1%
709800 20
< 0.1%
649900 3
 
< 0.1%

price_badge
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing260975
Missing (%)100.0%
Memory size4.0 MiB

trim
Text

MISSING 

Distinct3353
Distinct (%)1.3%
Missing4179
Missing (%)1.6%
Memory size4.0 MiB
2024-07-21T08:40:44.228891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length77
Median length69
Mean length7.8425637
Min length1

Characters and Unicode

Total characters2013939
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique446 ?
Unique (%)0.2%

Sample

1st rowRS
2nd rowHigh Roof
3rd rowHigh Roof
4th rowEX
5th rowLS
ValueCountFrequency (%)
base 25056
 
6.2%
premium 15644
 
3.8%
s 14291
 
3.5%
limited 12444
 
3.1%
se 11998
 
3.0%
sport 9192
 
2.3%
sel 9018
 
2.2%
4matic 8860
 
2.2%
plus 7035
 
1.7%
2.0t 6286
 
1.5%
Other values (1709) 286741
70.5%
2024-07-21T08:40:44.715898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 161162
 
8.0%
149255
 
7.4%
i 120886
 
6.0%
r 102626
 
5.1%
S 89945
 
4.5%
a 89847
 
4.5%
L 80364
 
4.0%
T 75875
 
3.8%
t 66662
 
3.3%
o 63432
 
3.1%
Other values (71) 1013885
50.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2013939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 161162
 
8.0%
149255
 
7.4%
i 120886
 
6.0%
r 102626
 
5.1%
S 89945
 
4.5%
a 89847
 
4.5%
L 80364
 
4.0%
T 75875
 
3.8%
t 66662
 
3.3%
o 63432
 
3.1%
Other values (71) 1013885
50.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2013939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 161162
 
8.0%
149255
 
7.4%
i 120886
 
6.0%
r 102626
 
5.1%
S 89945
 
4.5%
a 89847
 
4.5%
L 80364
 
4.0%
T 75875
 
3.8%
t 66662
 
3.3%
o 63432
 
3.1%
Other values (71) 1013885
50.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2013939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 161162
 
8.0%
149255
 
7.4%
i 120886
 
6.0%
r 102626
 
5.1%
S 89945
 
4.5%
a 89847
 
4.5%
L 80364
 
4.0%
T 75875
 
3.8%
t 66662
 
3.3%
o 63432
 
3.1%
Other values (71) 1013885
50.3%

drivetrain
Categorical

IMBALANCE 

Distinct18
Distinct (%)< 0.1%
Missing1443
Missing (%)0.6%
Memory size4.0 MiB
All-wheel Drive
121472 
Front-wheel Drive
55003 
Four-wheel Drive
47188 
Rear-wheel Drive
29210 
AWD
 
3388
Other values (13)
 
3271

Length

Max length58
Median length17
Mean length15.431134
Min length3

Characters and Unicode

Total characters4004873
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAll-wheel Drive
2nd rowFront-wheel Drive
3rd rowRear-wheel Drive
4th rowFront-wheel Drive
5th rowFront-wheel Drive

Common Values

ValueCountFrequency (%)
All-wheel Drive 121472
46.5%
Front-wheel Drive 55003
21.1%
Four-wheel Drive 47188
 
18.1%
Rear-wheel Drive 29210
 
11.2%
AWD 3388
 
1.3%
FWD 1281
 
0.5%
Unknown 899
 
0.3%
4WD 666
 
0.3%
RWD 273
 
0.1%
All Wheel Drive 51
 
< 0.1%
Other values (8) 101
 
< 0.1%
(Missing) 1443
 
0.6%

Length

2024-07-21T08:40:44.857839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drive 253010
49.4%
all-wheel 121476
23.7%
front-wheel 55003
 
10.7%
four-wheel 47188
 
9.2%
rear-wheel 29210
 
5.7%
awd 3388
 
0.7%
fwd 1281
 
0.2%
unknown 899
 
0.2%
4wd 666
 
0.1%
rwd 273
 
0.1%
Other values (13) 286
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 788274
19.7%
l 496066
12.4%
r 384494
9.6%
D 258625
 
6.5%
w 253773
 
6.3%
253148
 
6.3%
i 253017
 
6.3%
h 253011
 
6.3%
v 253010
 
6.3%
- 252878
 
6.3%
Other values (26) 558577
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4004873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 788274
19.7%
l 496066
12.4%
r 384494
9.6%
D 258625
 
6.5%
w 253773
 
6.3%
253148
 
6.3%
i 253017
 
6.3%
h 253011
 
6.3%
v 253010
 
6.3%
- 252878
 
6.3%
Other values (26) 558577
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4004873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 788274
19.7%
l 496066
12.4%
r 384494
9.6%
D 258625
 
6.5%
w 253773
 
6.3%
253148
 
6.3%
i 253017
 
6.3%
h 253011
 
6.3%
v 253010
 
6.3%
- 252878
 
6.3%
Other values (26) 558577
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4004873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 788274
19.7%
l 496066
12.4%
r 384494
9.6%
D 258625
 
6.5%
w 253773
 
6.3%
253148
 
6.3%
i 253017
 
6.3%
h 253011
 
6.3%
v 253010
 
6.3%
- 252878
 
6.3%
Other values (26) 558577
13.9%

dealer_name
Text

MISSING 

Distinct952
Distinct (%)0.4%
Missing2693
Missing (%)1.0%
Memory size4.0 MiB
2024-07-21T08:40:45.097223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length86
Median length56
Mean length23.972154
Min length6

Characters and Unicode

Total characters6191576
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique383 ?
Unique (%)0.1%

Sample

1st rowCastle Rock Chevrolet GMC
2nd rowNew Smyrna Chrysler Jeep Dodge RAM
3rd rowMercedes-Benz of Farmington
4th rowKingman Honda
5th rowMcSweeney Chevrolet GMC Clanton
ValueCountFrequency (%)
of 80208
 
8.4%
dodge 23981
 
2.5%
chrysler 23363
 
2.5%
jeep 23356
 
2.5%
ram 23353
 
2.5%
chicago 23165
 
2.4%
auto 22763
 
2.4%
ford 21291
 
2.2%
chevrolet 18413
 
1.9%
motors 15147
 
1.6%
Other values (991) 676561
71.1%
2024-07-21T08:40:45.527210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
693319
 
11.2%
e 502694
 
8.1%
o 487649
 
7.9%
a 411124
 
6.6%
r 371816
 
6.0%
l 274085
 
4.4%
n 273627
 
4.4%
t 260673
 
4.2%
i 258528
 
4.2%
s 241579
 
3.9%
Other values (59) 2416482
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6191576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
693319
 
11.2%
e 502694
 
8.1%
o 487649
 
7.9%
a 411124
 
6.6%
r 371816
 
6.0%
l 274085
 
4.4%
n 273627
 
4.4%
t 260673
 
4.2%
i 258528
 
4.2%
s 241579
 
3.9%
Other values (59) 2416482
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6191576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
693319
 
11.2%
e 502694
 
8.1%
o 487649
 
7.9%
a 411124
 
6.6%
r 371816
 
6.0%
l 274085
 
4.4%
n 273627
 
4.4%
t 260673
 
4.2%
i 258528
 
4.2%
s 241579
 
3.9%
Other values (59) 2416482
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6191576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
693319
 
11.2%
e 502694
 
8.1%
o 487649
 
7.9%
a 411124
 
6.6%
r 371816
 
6.0%
l 274085
 
4.4%
n 273627
 
4.4%
t 260673
 
4.2%
i 258528
 
4.2%
s 241579
 
3.9%
Other values (59) 2416482
39.0%

dealer_zip
Real number (ℝ)

MISSING 

Distinct575
Distinct (%)0.2%
Missing2693
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean59744.542
Minimum1020
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:45.678646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile60004
Q160126
median60445
Q360532
95-th percentile60659
Maximum99301
Range98281
Interquartile range (IQR)406

Descriptive statistics

Standard deviation3193.3773
Coefficient of variation (CV)0.053450527
Kurtosis44.540649
Mean59744.542
Median Absolute Deviation (MAD)197
Skewness-4.7385917
Sum1.543094 × 1010
Variance10197658
MonotonicityNot monotonic
2024-07-21T08:40:45.821498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60540 16462
 
6.3%
60515 12635
 
4.8%
60525 9819
 
3.8%
60126 8535
 
3.3%
46322 7586
 
2.9%
60453 7027
 
2.7%
60035 6877
 
2.6%
60477 6856
 
2.6%
60559 6793
 
2.6%
60462 5902
 
2.3%
Other values (565) 169790
65.1%
ValueCountFrequency (%)
1020 3
< 0.1%
1060 3
< 0.1%
1201 1
 
< 0.1%
2135 1
 
< 0.1%
2601 2
< 0.1%
2717 1
 
< 0.1%
2886 1
 
< 0.1%
2891 1
 
< 0.1%
3060 1
 
< 0.1%
3103 1
 
< 0.1%
ValueCountFrequency (%)
99301 1
< 0.1%
99201 2
< 0.1%
99019 1
< 0.1%
98837 1
< 0.1%
98037 1
< 0.1%
98036 1
< 0.1%
97702 1
< 0.1%
97401 1
< 0.1%
97225 1
< 0.1%
97005 2
< 0.1%

mileage
Real number (ℝ)

MISSING  ZEROS 

Distinct38534
Distinct (%)15.1%
Missing5943
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean30231.074
Minimum0
Maximum962839
Zeros10145
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:46.081354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median4197
Q351569
95-th percentile118540.25
Maximum962839
Range962839
Interquartile range (IQR)51562

Descriptive statistics

Standard deviation42813.013
Coefficient of variation (CV)1.4161923
Kurtosis3.9326183
Mean30231.074
Median Absolute Deviation (MAD)4197
Skewness1.697265
Sum7.7098913 × 109
Variance1.8329541 × 109
MonotonicityNot monotonic
2024-07-21T08:40:46.227187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 16127
 
6.2%
10 15735
 
6.0%
0 10145
 
3.9%
3 9056
 
3.5%
6 8519
 
3.3%
2 6694
 
2.6%
7 5760
 
2.2%
1 5549
 
2.1%
11 5401
 
2.1%
4 4858
 
1.9%
Other values (38524) 167188
64.1%
(Missing) 5943
 
2.3%
ValueCountFrequency (%)
0 10145
3.9%
1 5549
 
2.1%
2 6694
2.6%
3 9056
3.5%
4 4858
 
1.9%
5 16127
6.2%
6 8519
3.3%
7 5760
 
2.2%
8 3931
 
1.5%
9 3802
 
1.5%
ValueCountFrequency (%)
962839 1
 
< 0.1%
440911 2
 
< 0.1%
426586 3
 
< 0.1%
403877 4
 
< 0.1%
398677 3
 
< 0.1%
385223 10
< 0.1%
376000 1
 
< 0.1%
358331 2
 
< 0.1%
350017 5
< 0.1%
344541 1
 
< 0.1%

make
Text

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:46.435245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.2382987
Min length2

Characters and Unicode

Total characters1628040
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowChevrolet
2nd rowRAM
3rd rowMercedes-Benz
4th rowHonda
5th rowChevrolet
ValueCountFrequency (%)
ford 25474
 
9.6%
chevrolet 22427
 
8.5%
mercedes-benz 17235
 
6.5%
jeep 16495
 
6.2%
bmw 14963
 
5.6%
nissan 14876
 
5.6%
hyundai 12647
 
4.8%
volkswagen 11681
 
4.4%
audi 9469
 
3.6%
honda 8662
 
3.3%
Other values (60) 111460
42.0%
2024-07-21T08:40:46.770640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 188957
 
11.6%
a 120402
 
7.4%
o 112268
 
6.9%
d 97449
 
6.0%
r 96956
 
6.0%
n 81112
 
5.0%
s 78461
 
4.8%
i 69504
 
4.3%
l 62983
 
3.9%
u 57513
 
3.5%
Other values (37) 662435
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1628040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 188957
 
11.6%
a 120402
 
7.4%
o 112268
 
6.9%
d 97449
 
6.0%
r 96956
 
6.0%
n 81112
 
5.0%
s 78461
 
4.8%
i 69504
 
4.3%
l 62983
 
3.9%
u 57513
 
3.5%
Other values (37) 662435
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1628040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 188957
 
11.6%
a 120402
 
7.4%
o 112268
 
6.9%
d 97449
 
6.0%
r 96956
 
6.0%
n 81112
 
5.0%
s 78461
 
4.8%
i 69504
 
4.3%
l 62983
 
3.9%
u 57513
 
3.5%
Other values (37) 662435
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1628040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 188957
 
11.6%
a 120402
 
7.4%
o 112268
 
6.9%
d 97449
 
6.0%
r 96956
 
6.0%
n 81112
 
5.0%
s 78461
 
4.8%
i 69504
 
4.3%
l 62983
 
3.9%
u 57513
 
3.5%
Other values (37) 662435
40.7%

bodystyle
Categorical

Distinct10
Distinct (%)< 0.1%
Missing1263
Missing (%)0.5%
Memory size4.0 MiB
SUV
145431 
Sedan
45761 
Pickup Truck
21495 
Coupe
 
12193
Cargo Van
 
10531
Other values (5)
24301 

Length

Max length13
Median length3
Mean length5.0603091
Min length3

Characters and Unicode

Total characters1314223
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowCargo Van
3rd rowCargo Van
4th rowSUV
5th rowSUV

Common Values

ValueCountFrequency (%)
SUV 145431
55.7%
Sedan 45761
 
17.5%
Pickup Truck 21495
 
8.2%
Coupe 12193
 
4.7%
Cargo Van 10531
 
4.0%
Hatchback 9301
 
3.6%
Convertible 8325
 
3.2%
Passenger Van 3028
 
1.2%
Wagon 2368
 
0.9%
Minivan 1279
 
0.5%
(Missing) 1263
 
0.5%

Length

2024-07-21T08:40:46.908409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-21T08:40:47.026139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
suv 145431
49.3%
sedan 45761
 
15.5%
pickup 21495
 
7.3%
truck 21495
 
7.3%
van 13559
 
4.6%
coupe 12193
 
4.1%
cargo 10531
 
3.6%
hatchback 9301
 
3.2%
convertible 8325
 
2.8%
passenger 3028
 
1.0%
Other values (2) 3647
 
1.2%

Most occurring characters

ValueCountFrequency (%)
S 191192
14.5%
V 158990
12.1%
U 145431
11.1%
a 95128
 
7.2%
e 80660
 
6.1%
n 75599
 
5.8%
c 61592
 
4.7%
u 55183
 
4.2%
k 52291
 
4.0%
d 45761
 
3.5%
Other values (18) 352396
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 191192
14.5%
V 158990
12.1%
U 145431
11.1%
a 95128
 
7.2%
e 80660
 
6.1%
n 75599
 
5.8%
c 61592
 
4.7%
u 55183
 
4.2%
k 52291
 
4.0%
d 45761
 
3.5%
Other values (18) 352396
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 191192
14.5%
V 158990
12.1%
U 145431
11.1%
a 95128
 
7.2%
e 80660
 
6.1%
n 75599
 
5.8%
c 61592
 
4.7%
u 55183
 
4.2%
k 52291
 
4.0%
d 45761
 
3.5%
Other values (18) 352396
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 191192
14.5%
V 158990
12.1%
U 145431
11.1%
a 95128
 
7.2%
e 80660
 
6.1%
n 75599
 
5.8%
c 61592
 
4.7%
u 55183
 
4.2%
k 52291
 
4.0%
d 45761
 
3.5%
Other values (18) 352396
26.8%

cat
Categorical

MISSING 

Distinct39
Distinct (%)< 0.1%
Missing4057
Missing (%)1.6%
Memory size4.0 MiB
crossover_compact
44405 
luxurysuv_crossover
33734 
crossover_midsize
20780 
suv_midsize
17082 
truck_fullsize
15057 
Other values (34)
125860 

Length

Max length28
Median length24
Mean length16.542753
Min length8

Characters and Unicode

Total characters4250131
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowev_crossover_midsize
2nd rowvan_fullsize
3rd rowvan_fullsize
4th rowcrossover_compact
5th rowcrossover_midsize

Common Values

ValueCountFrequency (%)
crossover_compact 44405
17.0%
luxurysuv_crossover 33734
12.9%
crossover_midsize 20780
 
8.0%
suv_midsize 17082
 
6.5%
truck_fullsize 15057
 
5.8%
luxurypassenger_standard 13441
 
5.2%
van_fullsize 12391
 
4.7%
sedan_compact 12185
 
4.7%
sedan_midsize 9878
 
3.8%
luxurypassenger_plus 9602
 
3.7%
Other values (29) 68363
26.2%

Length

2024-07-21T08:40:47.180297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crossover_compact 44405
17.3%
luxurysuv_crossover 33734
13.1%
crossover_midsize 20780
 
8.1%
suv_midsize 17082
 
6.6%
truck_fullsize 15057
 
5.9%
luxurypassenger_standard 13441
 
5.2%
van_fullsize 12391
 
4.8%
sedan_compact 12185
 
4.7%
sedan_midsize 9878
 
3.8%
luxurypassenger_plus 9602
 
3.7%
Other values (29) 68363
26.6%

Most occurring characters

ValueCountFrequency (%)
s 499915
11.8%
r 379911
 
8.9%
e 339142
 
8.0%
o 316501
 
7.4%
u 311246
 
7.3%
c 307441
 
7.2%
_ 270578
 
6.4%
v 232436
 
5.5%
a 202514
 
4.8%
i 175570
 
4.1%
Other values (15) 1214877
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4250131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 499915
11.8%
r 379911
 
8.9%
e 339142
 
8.0%
o 316501
 
7.4%
u 311246
 
7.3%
c 307441
 
7.2%
_ 270578
 
6.4%
v 232436
 
5.5%
a 202514
 
4.8%
i 175570
 
4.1%
Other values (15) 1214877
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4250131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 499915
11.8%
r 379911
 
8.9%
e 339142
 
8.0%
o 316501
 
7.4%
u 311246
 
7.3%
c 307441
 
7.2%
_ 270578
 
6.4%
v 232436
 
5.5%
a 202514
 
4.8%
i 175570
 
4.1%
Other values (15) 1214877
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4250131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 499915
11.8%
r 379911
 
8.9%
e 339142
 
8.0%
o 316501
 
7.4%
u 311246
 
7.3%
c 307441
 
7.2%
_ 270578
 
6.4%
v 232436
 
5.5%
a 202514
 
4.8%
i 175570
 
4.1%
Other values (15) 1214877
28.6%

vin
Text

Distinct89244
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:47.463031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.991911
Min length7

Characters and Unicode

Total characters4434464
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30351 ?
Unique (%)11.6%

Sample

1st row3GNKDCRJ6RS227894
2nd row3C6LRVDG0RE118763
3rd rowW1Y4KCHY8RT178723
4th row5J6RS3H44RL004214
5th row3GNAXHEG1RL299011
ValueCountFrequency (%)
scedt26t9bd002935 40
 
< 0.1%
1g6kw5rj0lu103290 39
 
< 0.1%
sbm13gaa0jw005233 38
 
< 0.1%
wba7j2c57jg938534 38
 
< 0.1%
scfac23363b500753 38
 
< 0.1%
zffkc33c000083652 38
 
< 0.1%
sbm16aea7pw001459 38
 
< 0.1%
2ckdl43f686065575 38
 
< 0.1%
wbaeg2324pcb74981 38
 
< 0.1%
sbm11raa1gw675304 37
 
< 0.1%
Other values (89234) 260593
99.9%
2024-07-21T08:40:47.845516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 364226
 
8.2%
3 267112
 
6.0%
0 260071
 
5.9%
2 255802
 
5.8%
4 246164
 
5.6%
5 245138
 
5.5%
7 201173
 
4.5%
6 198119
 
4.5%
8 192992
 
4.4%
R 185508
 
4.2%
Other values (26) 2018159
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4434464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 364226
 
8.2%
3 267112
 
6.0%
0 260071
 
5.9%
2 255802
 
5.8%
4 246164
 
5.6%
5 245138
 
5.5%
7 201173
 
4.5%
6 198119
 
4.5%
8 192992
 
4.4%
R 185508
 
4.2%
Other values (26) 2018159
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4434464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 364226
 
8.2%
3 267112
 
6.0%
0 260071
 
5.9%
2 255802
 
5.8%
4 246164
 
5.6%
5 245138
 
5.5%
7 201173
 
4.5%
6 198119
 
4.5%
8 192992
 
4.4%
R 185508
 
4.2%
Other values (26) 2018159
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4434464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 364226
 
8.2%
3 267112
 
6.0%
0 260071
 
5.9%
2 255802
 
5.8%
4 246164
 
5.6%
5 245138
 
5.5%
7 201173
 
4.5%
6 198119
 
4.5%
8 192992
 
4.4%
R 185508
 
4.2%
Other values (26) 2018159
45.5%
Distinct6979
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-07-21T08:40:48.135216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length97
Median length87
Mean length23.227248
Min length6

Characters and Unicode

Total characters6061731
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique797 ?
Unique (%)0.3%

Sample

1st rowChevrolet:Blazer EV:RS
2nd rowRAM:ProMaster 2500:High Roof
3rd rowMercedes-Benz:Sprinter 2500:High Roof
4th rowHonda:CR-V:EX
5th rowChevrolet:Equinox:LS
ValueCountFrequency (%)
s 8120
 
1.6%
premium 8074
 
1.6%
plus 6916
 
1.4%
4matic 6783
 
1.3%
jeep:grand 6340
 
1.3%
se 6206
 
1.2%
roof 5558
 
1.1%
package 4030
 
0.8%
chevrolet:silverado 3925
 
0.8%
awd 3845
 
0.8%
Other values (5476) 447281
88.2%
2024-07-21T08:40:48.594653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 521953
 
8.6%
e 482194
 
8.0%
a 350510
 
5.8%
r 337781
 
5.6%
o 273351
 
4.5%
i 261702
 
4.3%
245589
 
4.1%
n 213922
 
3.5%
s 193953
 
3.2%
d 171756
 
2.8%
Other values (71) 3009020
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6061731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 521953
 
8.6%
e 482194
 
8.0%
a 350510
 
5.8%
r 337781
 
5.6%
o 273351
 
4.5%
i 261702
 
4.3%
245589
 
4.1%
n 213922
 
3.5%
s 193953
 
3.2%
d 171756
 
2.8%
Other values (71) 3009020
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6061731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 521953
 
8.6%
e 482194
 
8.0%
a 350510
 
5.8%
r 337781
 
5.6%
o 273351
 
4.5%
i 261702
 
4.3%
245589
 
4.1%
n 213922
 
3.5%
s 193953
 
3.2%
d 171756
 
2.8%
Other values (71) 3009020
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6061731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 521953
 
8.6%
e 482194
 
8.0%
a 350510
 
5.8%
r 337781
 
5.6%
o 273351
 
4.5%
i 261702
 
4.3%
245589
 
4.1%
n 213922
 
3.5%
s 193953
 
3.2%
d 171756
 
2.8%
Other values (71) 3009020
49.6%

fuel_type
Categorical

IMBALANCE 

Distinct24
Distinct (%)< 0.1%
Missing2232
Missing (%)0.9%
Memory size4.0 MiB
Gasoline
222207 
Electric
 
14128
Hybrid
 
11297
Diesel
 
6930
E85 Flex Fuel
 
3666
Other values (19)
 
515

Length

Max length29
Median length8
Mean length7.9466923
Min length6

Characters and Unicode

Total characters2056151
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowElectric
2nd rowGasoline
3rd rowDiesel
4th rowGasoline
5th rowGasoline

Common Values

ValueCountFrequency (%)
Gasoline 222207
85.1%
Electric 14128
 
5.4%
Hybrid 11297
 
4.3%
Diesel 6930
 
2.7%
E85 Flex Fuel 3666
 
1.4%
Plug-In Hybrid 103
 
< 0.1%
Regular Unleaded 62
 
< 0.1%
Gasoline/Mild Electric Hy 58
 
< 0.1%
Electric and Gas Hybrid 46
 
< 0.1%
Bio Diesel 38
 
< 0.1%
Other values (14) 208
 
0.1%
(Missing) 2232
 
0.9%

Length

2024-07-21T08:40:48.739472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gasoline 222237
83.3%
electric 14278
 
5.4%
hybrid 11460
 
4.3%
diesel 6981
 
2.6%
fuel 3736
 
1.4%
flex 3667
 
1.4%
e85 3666
 
1.4%
plug-in 108
 
< 0.1%
unleaded 89
 
< 0.1%
gasoline/mild 69
 
< 0.1%
Other values (18) 497
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 258334
12.6%
i 255275
12.4%
l 251409
12.2%
s 229390
11.2%
a 222680
10.8%
n 222556
10.8%
G 222433
10.8%
o 222348
10.8%
c 28630
 
1.4%
r 25864
 
1.3%
Other values (32) 117232
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2056151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 258334
12.6%
i 255275
12.4%
l 251409
12.2%
s 229390
11.2%
a 222680
10.8%
n 222556
10.8%
G 222433
10.8%
o 222348
10.8%
c 28630
 
1.4%
r 25864
 
1.3%
Other values (32) 117232
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2056151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 258334
12.6%
i 255275
12.4%
l 251409
12.2%
s 229390
11.2%
a 222680
10.8%
n 222556
10.8%
G 222433
10.8%
o 222348
10.8%
c 28630
 
1.4%
r 25864
 
1.3%
Other values (32) 117232
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2056151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 258334
12.6%
i 255275
12.4%
l 251409
12.2%
s 229390
11.2%
a 222680
10.8%
n 222556
10.8%
G 222433
10.8%
o 222348
10.8%
c 28630
 
1.4%
r 25864
 
1.3%
Other values (32) 117232
5.7%

stock_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Used
134367 
New
126608 

Length

Max length4
Median length4
Mean length3.5148654
Min length3

Characters and Unicode

Total characters917292
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew
2nd rowNew
3rd rowNew
4th rowNew
5th rowNew

Common Values

ValueCountFrequency (%)
Used 134367
51.5%
New 126608
48.5%

Length

2024-07-21T08:40:48.857816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-21T08:40:48.953334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
used 134367
51.5%
new 126608
48.5%

Most occurring characters

ValueCountFrequency (%)
e 260975
28.5%
U 134367
14.6%
s 134367
14.6%
d 134367
14.6%
N 126608
13.8%
w 126608
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 917292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 260975
28.5%
U 134367
14.6%
s 134367
14.6%
d 134367
14.6%
N 126608
13.8%
w 126608
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 917292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 260975
28.5%
U 134367
14.6%
s 134367
14.6%
d 134367
14.6%
N 126608
13.8%
w 126608
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 917292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 260975
28.5%
U 134367
14.6%
s 134367
14.6%
d 134367
14.6%
N 126608
13.8%
w 126608
13.8%

exterior_color
Text

MISSING 

Distinct3927
Distinct (%)1.5%
Missing3285
Missing (%)1.3%
Memory size4.0 MiB
2024-07-21T08:40:49.195639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length54
Median length46
Mean length15.053425
Min length1

Characters and Unicode

Total characters3879117
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique533 ?
Unique (%)0.2%

Sample

1st rowSterling Gray Metallic
2nd rowBright White Clearcoat
3rd rowBlue Grey
4th rowRadiant Red Metallic
5th rowSummit White
ValueCountFrequency (%)
metallic 79920
 
13.4%
white 62514
 
10.5%
black 58825
 
9.9%
gray 33983
 
5.7%
blue 22334
 
3.7%
silver 22093
 
3.7%
pearl 21817
 
3.7%
clearcoat 18456
 
3.1%
red 15708
 
2.6%
crystal 11669
 
2.0%
Other values (1813) 249099
41.8%
2024-07-21T08:40:49.629647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 384301
 
9.9%
a 371242
 
9.6%
e 367120
 
9.5%
338741
 
8.7%
i 301201
 
7.8%
t 285091
 
7.3%
r 216862
 
5.6%
c 205392
 
5.3%
M 103580
 
2.7%
B 102099
 
2.6%
Other values (68) 1203488
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3879117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 384301
 
9.9%
a 371242
 
9.6%
e 367120
 
9.5%
338741
 
8.7%
i 301201
 
7.8%
t 285091
 
7.3%
r 216862
 
5.6%
c 205392
 
5.3%
M 103580
 
2.7%
B 102099
 
2.6%
Other values (68) 1203488
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3879117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 384301
 
9.9%
a 371242
 
9.6%
e 367120
 
9.5%
338741
 
8.7%
i 301201
 
7.8%
t 285091
 
7.3%
r 216862
 
5.6%
c 205392
 
5.3%
M 103580
 
2.7%
B 102099
 
2.6%
Other values (68) 1203488
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3879117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 384301
 
9.9%
a 371242
 
9.6%
e 367120
 
9.5%
338741
 
8.7%
i 301201
 
7.8%
t 285091
 
7.3%
r 216862
 
5.6%
c 205392
 
5.3%
M 103580
 
2.7%
B 102099
 
2.6%
Other values (68) 1203488
31.0%

page_channel
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
shopping
260975 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2087800
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshopping
2nd rowshopping
3rd rowshopping
4th rowshopping
5th rowshopping

Common Values

ValueCountFrequency (%)
shopping 260975
100.0%

Length

2024-07-21T08:40:49.767100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-21T08:40:49.853096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
shopping 260975
100.0%

Most occurring characters

ValueCountFrequency (%)
p 521950
25.0%
s 260975
12.5%
h 260975
12.5%
o 260975
12.5%
i 260975
12.5%
n 260975
12.5%
g 260975
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 521950
25.0%
s 260975
12.5%
h 260975
12.5%
o 260975
12.5%
i 260975
12.5%
n 260975
12.5%
g 260975
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 521950
25.0%
s 260975
12.5%
h 260975
12.5%
o 260975
12.5%
i 260975
12.5%
n 260975
12.5%
g 260975
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2087800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 521950
25.0%
s 260975
12.5%
h 260975
12.5%
o 260975
12.5%
i 260975
12.5%
n 260975
12.5%
g 260975
12.5%

Interactions

2024-07-21T08:40:36.275814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:33.878397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.554725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.135318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.697030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.393781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.020621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.672291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.249167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.816541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.508836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.217956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.785720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.363692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.931120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.624544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.332736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.906003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.472383image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.047826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.742690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:34.442997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.024987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:35.584182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-21T08:40:36.163262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-07-21T08:40:36.991997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-21T08:40:37.597210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-21T08:40:38.610862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

listing_idmsrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
02f77722f-3a80-4960-bd04-4859b4df975e57215.02024Chevrolet:Blazer EV:RS:2024Blazer EVNaNBlacknational54595.0NaNRSAll-wheel DriveCastle Rock Chevrolet GMC80104.00.0ChevroletSUVev_crossover_midsize3GNKDCRJ6RS227894Chevrolet:Blazer EV:RSElectricNewSterling Gray Metallicshopping
1067f9671-3672-4d0a-afe8-110b905bed3a58845.02024RAM:ProMaster 2500:High Roof:2024ProMaster 2500NaNBlacknational52446.0NaNHigh RoofFront-wheel DriveNew Smyrna Chrysler Jeep Dodge RAM32168.00.0RAMCargo Vanvan_fullsize3C6LRVDG0RE118763RAM:ProMaster 2500:High RoofGasolineNewBright White Clearcoatshopping
20a24aeeb-112c-4a6f-b932-07504e82dacb58795.02024Mercedes-Benz:Sprinter 2500:High Roof:2024Sprinter 2500NaNNaNnational54295.0NaNHigh RoofRear-wheel DriveMercedes-Benz of Farmington84025.08.0Mercedes-BenzCargo Vanvan_fullsizeW1Y4KCHY8RT178723Mercedes-Benz:Sprinter 2500:High RoofDieselNewBlue Greyshopping
32636cae0-4081-4ce3-8940-5687e8ada12933815.02024Honda:CR-V:EX:2024CR-VNaNGraynationalNaNNaNEXFront-wheel DriveKingman Honda86409.07.0HondaSUVcrossover_compact5J6RS3H44RL004214Honda:CR-V:EXGasolineNewRadiant Red Metallicshopping
4b443a216-8ea4-4e1c-b5f3-a6210d6f95e927995.02024Chevrolet:Equinox:LS:2024EquinoxNaNMedium Ash Graynational24803.0NaNLSFront-wheel DriveMcSweeney Chevrolet GMC Clanton35045.00.0ChevroletSUVcrossover_midsize3GNAXHEG1RL299011Chevrolet:Equinox:LSGasolineNewSummit Whiteshopping
59d076ff4-8598-43fa-9dda-0b29e82df83983630.02024Audi:Q8 e-tron:Premium:2024Q8 e-tronNaNPearl Beigenational83630.0NaNPremiumAll-wheel DriveAudi Stuart34997.020.0AudiSUVev_crossover_midsizeWA15AAGE4RB021424Audi:Q8 e-tron:PremiumElectricNewGlacier White Metallicshopping
63110196b-8104-49f9-a176-7f1de500ffde33610.02024Mitsubishi:Eclipse Cross:SEL:2024Eclipse CrossNaNGraynational33610.0NaNSELFour-wheel DriveMcClinton Auto Group26101.05.0MitsubishiSUVcrossover_compactJA4ATWAA2RZ046423Mitsubishi:Eclipse Cross:SELGasolineNewMercury Gray Metallicshopping
7c08081b6-68fa-4820-b5ff-b53730a6480950185.02024Dodge:Hornet:R/T Plus:2024HornetNaNBlacknational40185.0NaNR/T PlusAll-wheel DriveDon Jackson CDJR North30028.016.0DodgeSUVhybrid_suvZACPDFDW9R3A24025Dodge:Hornet:R/T PlusHybridNewBlue Steelshopping
819e8d9c0-6f63-4700-84fd-60ca232502e427825.02024Nissan:Kicks:SR:2024KicksNaNCharcoalnational27825.0NaNSRFront-wheel DriveHalladay Nissan82001.06.0NissanSUVcrossover_compact3N1CP5DV6RL526633Nissan:Kicks:SRGasolineNewScarlet Ember Tintcoatshopping
9c2df0bf7-c206-4f9e-bd34-4410aabde5f953727.02024Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-Line:2024Atlas Cross SportNaNBlack w/ Blue Crustnational50727.0NaN2.0T SEL Premium R-LineAll-wheel DriveAutoNation Volkswagen Las Vegas89146.010.0VolkswagenSUVsuv_midsize1V2FE2CA0RC238064Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-LineGasolineNewPlatinum Gray Metallicshopping
listing_idmsrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
260965efe10bbf-9fbf-42ab-9e96-a09c5e6991790.02016Cadillac:XTS:Luxury:2016XTSNaNJet Blacknational15450.0NaNLuxuryAll-wheel DriveRevved Motors60101.097580.0CadillacSedanluxurypassenger_plus2G61N5S35G9114248Cadillac:XTS:LuxuryGasolineUsedSummit Whiteshopping
260966a376ed8b-a7ad-43a4-b6c1-bc7ab99269d649700.02024Jeep:Grand Cherokee:Limited X:2024Grand CherokeeNaNB6x7national42386.0NaNLimited XFour-wheel DriveCastle Chrysler Dodge Jeep Ram of Naperville60540.010.0JeepSUVsuv_midsize1C4RJHBG2RC165945Jeep:Grand Cherokee:Limited XGasolineNewBright White Clearcoatshopping
26096782c83ed9-ecc2-4d9d-8ae3-44b436d06f8a0.02014Chevrolet:Traverse:1LT:2014TraverseNaNEbonynational11975.0NaN1LTFront-wheel DriveArlington Heights Buick GMC60004.099098.0ChevroletSUVcrossover_midsize1GNKRGKD7EJ367803Chevrolet:Traverse:1LTGasolineUsedWhite Diamond Tricoatshopping
2609683b2ee8c5-aea6-4aca-a0ba-5f8429bbc8fd35505.02024Ford:Bronco Sport:Big Bend:2024Bronco SportNaNEbonynational31813.0NaNBig BendFour-wheel DriveNapleton Ford of Oak Lawn60453.08.0FordSUVcrossover_compact3FMCR9B62RRE31084Ford:Bronco Sport:Big BendGasolineNewShadow Blackshopping
2609698135e3ea-77bc-43ac-bb72-f3605ae50deb38320.02024Ford:Bronco Sport:Outer Banks:2024Bronco SportNaNEbony/Roastnational35319.0NaNOuter BanksFour-wheel DriveFox Lincoln60647.02.0FordSUVcrossover_compact3FMCR9C6XRRF18701Ford:Bronco Sport:Outer BanksGasolineNewEruption Green Metallicshopping
2609706c208dd2-2b2d-4ecb-b28d-c18277d23b4f35655.02025Honda:CR-V:EX:2025CR-VNaNGraynational34044.0NaNEXAll-wheel DriveNapleton Honda of Morton Grove60053.013.0HondaSUVcrossover_compact7FARS4H4XSE007367Honda:CR-V:EXGasolineNewPlatinum White Pearlshopping
26097102f5a064-6abb-48dc-baf3-fcf5e59bad7a0.02023Genesis:G70:2.0T AWD:2023G70NaNObsidian Black/Red Stitchnational43498.0NaN2.0T AWDAll-wheel DriveGenesis of Schaumburg60173.01298.0GenesisSedanluxurypassenger_standardKMTG34TAXPU134326Genesis:G70:2.0T AWDGasolineUsedUyuni Whiteshopping
2609726b7d71a7-47d4-40f8-98b6-e900647da6e80.02018Audi:SQ5:3.0T Premium Plus:2018SQ5NaNBlack Quilted Leathernational36850.0NaN3.0T Premium PlusAll-wheel DriveWindy City Motors60639.068387.0AudiSUVluxurysuv_crossoverWA1A4AFY6J2025206Audi:SQ5:3.0T Premium PlusGasolineUsedFlorett Silver Metallicshopping
260973ae34d3b9-1857-4d40-8cff-cd6f446c39b452810.02024Jeep:Grand Cherokee:Limited X:2024Grand CherokeeNaNCapri Leathereete Seatsnational44310.0NaNLimited XFour-wheel DriveMarino Chrysler Jeep Dodge RAM60641.00.0JeepSUVsuv_midsize1C4RJHBG7RC159641Jeep:Grand Cherokee:Limited XGasolineNewDiamond Blackshopping
260974cb9939e1-9a71-4c4d-8931-f9dc4237ede246904.02024Kia:Sorento:SX:2024SorentoNaNBlacknational48415.0NaNSXAll-wheel DriveInternational Kia60487.06.0KiaSUVcrossover_midsize5XYRKDJF4RG305214Kia:Sorento:SXGasolineNewEbony Blackshopping

Duplicate rows

Most frequently occurring

listing_idmsrpyearcanonical_mmtymodelinterior_coloraff_codepricetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel# duplicates
81692211e8b3-3497-4491-91b8-3e54110223250.02010Lexus:HS 250h:Premium:2010HS 250hBlacknational9995.0PremiumFront-wheel DriveChicago Motors Direct60101.0128900.0LexusSedanhybrid_passengerJTHBB1BA9A2007709Lexus:HS 250h:PremiumHybridUsedBlack Opal Micashopping35
32900da5d4d7-714c-43d0-9cf7-c0e57895fbf8NaN1993BMW:850:Ci:1993850Blacknational44985.0CiNaNChicago Cars US60501.043506.0BMWCoupeNaNWBAEG2324PCB74981BMW:850:CiGasolineUsedBlackshopping34
42598b09bd8ed-0547-43fc-b1b7-062cbd1c58540.02006Mercedes-Benz:R-Class:5.0L:2006R-ClassTannational9999.05.0LFour-wheel DriveMy Choice Motors of Elmhurst60126.090021.0Mercedes-BenzNaNluxurysuv_crossover4JGCB75E16A003346Mercedes-Benz:R-Class:5.0LGasolineUsedGoldshopping34
000030870-19b8-4ead-8e68-9ce0ebfdae4dNaN2006Mazda:RX-8:Base:2006RX-8Blacknational11495.0BaseRear-wheel DriveOKAZ MOTORS60126.057950.0MazdaCoupecoupeconvertible_coupeJM1FE173360206245Mazda:RX-8:BaseGasolineUsedPhantom Blue Micashopping33
592618b331f2-b44e-46ce-9eaa-03d580b7d3470.02019Karma:Revero:Sedan:2019ReveroPalisades Sportnational55800.0SedanRear-wheel DriveChicago Motor Cars60185.04199.0KarmaSedanhybrid_passenger50GK41SA3KA000129Karma:Revero:SedanElectric with GaUsedSurf Whiteshopping33
304757df6bb71-ed0a-4cca-8307-a8948dd43131NaN2013BMW:ActiveHybrid 3:Base:2013ActiveHybrid 3Blacknational10985.0BaseRear-wheel DriveXchange Motors60101.0130268.0BMWSedanhybrid_passengerWBA3F9C52DF145158BMW:ActiveHybrid 3:BaseHybridUsedSilvershopping33
39064a1e668bf-4e86-4091-8af6-a7233bf2f708NaN1989Cadillac:Allante::1989AllanteBeigenational9995.0NaNFront-wheel DriveLuxury Car Outlet60185.030000.0CadillacCoupeNaN1G6VR3184KU100277Cadillac:Allante:GasolineUsedRedshopping33
1995752ab2ac9-447b-4721-9d8e-8056b99182750.02004BMW:745:745i:2004745Tannational7999.0745iRear-wheel DriveMy Choice Motors of Elmhurst60126.0119263.0BMWSedanluxurypassenger_plusWBAGL63424DP69960BMW:745:745iGasolineUsedGoldshopping32
2808773e1caa8-616e-4343-81ef-b3d2a34a136f0.01996Chevrolet:Caprice Classic:SS:1996Caprice ClassicGray Leathernational69800.0SSRear-wheel DriveChicago Motor Cars60185.05079.0ChevroletSedanNaN1G1BL52P2TR154578Chevrolet:Caprice Classic:SSGasolineUsedDark Green Gray Metallicshopping32
48680c97332d8-c3b4-4489-854c-5147c18915e70.02006Jaguar:X-Type:3.0L AWD 4dr Sedan:2006X-TypeBeigenational8995.03.0L AWD 4dr SedanAll-wheel DriveMoto Zone Inc.60160.059242.0JaguarSedanluxurypassenger_standardSAJWA51A66WE98033Jaguar:X-Type:3.0L AWD 4dr SedanGasolineUsedBlackshopping32